title: Benchmarking HEp-2 cells classification methods.
abstract: In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.
authors.forename: Pasquale
authors.initials: P
authors.lastname: Foggia
authors.forename: Gennaro
authors.initials: G
authors.lastname: Percannella
authors.forename: Paolo
authors.initials: P
authors.lastname: Soda
authors.forename: Mario
authors.initials: M
authors.lastname: Vento
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000592:standards*; D019084:Fluorescent Antibody Technique, Indirect / Q000592:standards*; D056945:Hep G2 Cells / Q000145:classification*; D006801:Humans; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results